Overview

Dataset statistics

Number of variables12
Number of observations2191
Missing cells3549
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.5 KiB
Average record size in memory96.1 B

Variable types

DateTime1
Categorical1
Numeric10

Warnings

PM_RETIRO is highly correlated with PM_VALLECAS and 2 other fieldsHigh correlation
PM_VALLECAS is highly correlated with PM_RETIRO and 2 other fieldsHigh correlation
PM_CIUDADLINEAL is highly correlated with PM_RETIRO and 2 other fieldsHigh correlation
PM_CENTRO is highly correlated with PM_RETIRO and 2 other fieldsHigh correlation
DEW_POINT is highly correlated with TEMPERATUREHigh correlation
TEMPERATURE is highly correlated with DEW_POINTHigh correlation
PM_RETIRO has 1115 (50.9%) missing values Missing
PM_VALLECAS has 1250 (57.1%) missing values Missing
PM_CIUDADLINEAL has 1118 (51.0%) missing values Missing
PM_CENTRO has 36 (1.6%) missing values Missing
COMMULATIVE_PRECIPITATION is highly skewed (γ1 = 46.72257286) Skewed
FECHA has unique values Unique
COMMULATIVE_PRECIPITATION has 1742 (79.5%) zeros Zeros

Reproduction

Analysis started2021-05-04 15:09:55.839467
Analysis finished2021-05-04 15:10:41.425046
Duration45.59 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

FECHA
Date

UNIQUE

Distinct2191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
Minimum2010-01-01 00:00:00
Maximum2015-12-31 00:00:00
2021-05-04T10:10:41.617529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:41.835460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SEASON
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
2
552 
1
552 
3
546 
4
541 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2191
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
2552
25.2%
1552
25.2%
3546
24.9%
4541
24.7%
2021-05-04T10:10:42.256412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-04T10:10:42.368113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring characters

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2191
100.0%

Most frequent character per category

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common2191
100.0%

Most frequent character per script

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2191
100.0%

Most frequent character per block

ValueCountFrequency (%)
1552
25.2%
2552
25.2%
3546
24.9%
4541
24.7%

PM_RETIRO
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct997
Distinct (%)92.7%
Missing1115
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean89.44022664
Minimum3
Maximum564.7083333
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:42.551590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12.20833333
Q135.90056818
median67.5625
Q3120.8546196
95-th percentile243.8636364
Maximum564.7083333
Range561.7083333
Interquartile range (IQR)84.95405138

Descriptive statistics

Standard deviation74.48367962
Coefficient of variation (CV)0.8327760608
Kurtosis4.170472053
Mean89.44022664
Median Absolute Deviation (MAD)38.54166667
Skewness1.742136037
Sum96237.68387
Variance5547.81853
MonotocityNot monotonic
2021-05-04T10:10:42.829845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.8754
 
0.2%
14.3753
 
0.1%
26.565217393
 
0.1%
15.791666673
 
0.1%
18.833333333
 
0.1%
43.916666673
 
0.1%
29.6252
 
0.1%
64.708333332
 
0.1%
24.958333332
 
0.1%
56.666666672
 
0.1%
Other values (987)1049
47.9%
(Missing)1115
50.9%
ValueCountFrequency (%)
31
< 0.1%
3.2916666671
< 0.1%
3.5416666671
< 0.1%
3.5454545451
< 0.1%
3.6666666671
< 0.1%
ValueCountFrequency (%)
564.70833331
< 0.1%
488.91666671
< 0.1%
439.91666671
< 0.1%
416.66666671
< 0.1%
399.70833331
< 0.1%

PM_VALLECAS
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct880
Distinct (%)93.5%
Missing1250
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean92.34863193
Minimum4.733333333
Maximum593
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:43.058235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.733333333
5-th percentile13.70833333
Q140.625
median73.45833333
Q3120.4347826
95-th percentile239.8636364
Maximum593
Range588.2666667
Interquartile range (IQR)79.80978261

Descriptive statistics

Standard deviation75.48522701
Coefficient of variation (CV)0.8173941014
Kurtosis5.291003561
Mean92.34863193
Median Absolute Deviation (MAD)38.16666667
Skewness1.889165365
Sum86900.06264
Variance5698.019496
MonotocityNot monotonic
2021-05-04T10:10:43.260693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.04166673
 
0.1%
1123
 
0.1%
45.291666673
 
0.1%
553
 
0.1%
24.416666672
 
0.1%
116.70833332
 
0.1%
77.083333332
 
0.1%
22.3752
 
0.1%
63.8752
 
0.1%
50.291666672
 
0.1%
Other values (870)917
41.9%
(Missing)1250
57.1%
ValueCountFrequency (%)
4.7333333331
< 0.1%
4.91
< 0.1%
4.9583333331
< 0.1%
5.7826086961
< 0.1%
5.8571428571
< 0.1%
ValueCountFrequency (%)
5931
< 0.1%
518.0476191
< 0.1%
442.6251
< 0.1%
439.45833331
< 0.1%
397.58333331
< 0.1%

PM_CIUDADLINEAL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1002
Distinct (%)93.4%
Missing1118
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean88.92030568
Minimum3.842105263
Maximum510.0434783
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:43.466150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.842105263
5-th percentile12.075
Q135.38095238
median65.95454545
Q3118.0416667
95-th percentile250.1416667
Maximum510.0434783
Range506.201373
Interquartile range (IQR)82.66071429

Descriptive statistics

Standard deviation75.52464414
Coefficient of variation (CV)0.8493520525
Kurtosis3.778997768
Mean88.92030568
Median Absolute Deviation (MAD)37.45454545
Skewness1.753894613
Sum95411.488
Variance5703.971873
MonotocityNot monotonic
2021-05-04T10:10:43.693536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.583333333
 
0.1%
33.541666673
 
0.1%
23.666666673
 
0.1%
37.52
 
0.1%
23.583333332
 
0.1%
60.6252
 
0.1%
96.458333332
 
0.1%
57.708333332
 
0.1%
23.416666672
 
0.1%
126.752
 
0.1%
Other values (992)1050
47.9%
(Missing)1118
51.0%
ValueCountFrequency (%)
3.8421052631
< 0.1%
4.8571428571
< 0.1%
51
< 0.1%
5.8333333331
< 0.1%
5.8751
< 0.1%
ValueCountFrequency (%)
510.04347831
< 0.1%
466.79166671
< 0.1%
423.29166671
< 0.1%
417.46666671
< 0.1%
415.83333331
< 0.1%

PM_CENTRO
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1800
Distinct (%)83.5%
Missing36
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean95.90843922
Minimum3.181818182
Maximum568.5652174
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:43.902377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.181818182
5-th percentile15.45126812
Q139.75595238
median74.91666667
Q3125.8637821
95-th percentile251.4625
Maximum568.5652174
Range565.3833992
Interquartile range (IQR)86.10782967

Descriptive statistics

Standard deviation77.68231099
Coefficient of variation (CV)0.8099632485
Kurtosis3.809004034
Mean95.90843922
Median Absolute Deviation (MAD)41.16666667
Skewness1.69854957
Sum206682.6865
Variance6034.54144
MonotocityNot monotonic
2021-05-04T10:10:44.092385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.541666674
 
0.2%
22.041666674
 
0.2%
25.083333334
 
0.2%
14.416666673
 
0.1%
67.458333333
 
0.1%
43.416666673
 
0.1%
25.6253
 
0.1%
122.29166673
 
0.1%
54.6253
 
0.1%
40.958333333
 
0.1%
Other values (1790)2122
96.9%
(Missing)36
 
1.6%
ValueCountFrequency (%)
3.1818181821
< 0.1%
6.0833333331
< 0.1%
6.3333333331
< 0.1%
6.5416666671
< 0.1%
6.6251
< 0.1%
ValueCountFrequency (%)
568.56521741
< 0.1%
537.251
< 0.1%
492.751
< 0.1%
464.3751
< 0.1%
449.751
< 0.1%

DEW_POINT
Real number (ℝ)

HIGH CORRELATION

Distinct998
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.074797095
Minimum-33.33333333
Maximum26.20833333
Zeros2
Zeros (%)0.1%
Memory size17.2 KiB
2021-05-04T10:10:44.334489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-33.33333333
5-th percentile-20.29166667
Q1-9.75
median2.291666667
Q315.08333333
95-th percentile21.95833333
Maximum26.20833333
Range59.54166667
Interquartile range (IQR)24.83333333

Descriptive statistics

Standard deviation13.9572431
Coefficient of variation (CV)6.727040027
Kurtosis-1.218974946
Mean2.074797095
Median Absolute Deviation (MAD)12.45833333
Skewness-0.1381714527
Sum4545.880435
Variance194.8046351
MonotocityNot monotonic
2021-05-04T10:10:44.517031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.757
 
0.3%
18.958333337
 
0.3%
16.791666677
 
0.3%
16.916666677
 
0.3%
207
 
0.3%
21.257
 
0.3%
14.916666676
 
0.3%
19.833333336
 
0.3%
22.256
 
0.3%
-7.256
 
0.3%
Other values (988)2125
97.0%
ValueCountFrequency (%)
-33.333333331
< 0.1%
-31.708333331
< 0.1%
-27.458333331
< 0.1%
-27.208333331
< 0.1%
-26.6251
< 0.1%
ValueCountFrequency (%)
26.208333332
0.1%
25.6251
< 0.1%
25.3751
< 0.1%
25.333333331
< 0.1%
25.251
< 0.1%

HUMIDITY
Real number (ℝ≥0)

Distinct1295
Distinct (%)59.4%
Missing11
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean54.6590448
Minimum6.75
Maximum100
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:44.921916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.75
5-th percentile21.66458333
Q137.83333333
median55.08333333
Q370.95833333
95-th percentile86.71041667
Maximum100
Range93.25
Interquartile range (IQR)33.125

Descriptive statistics

Standard deviation20.29542131
Coefficient of variation (CV)0.3713094765
Kurtosis-0.933884127
Mean54.6590448
Median Absolute Deviation (MAD)16.41666667
Skewness-0.06153057447
Sum119156.7177
Variance411.9041261
MonotocityNot monotonic
2021-05-04T10:10:45.137342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
496
 
0.3%
58.458333336
 
0.3%
80.1256
 
0.3%
57.041666676
 
0.3%
37.583333335
 
0.2%
40.583333335
 
0.2%
35.166666675
 
0.2%
67.041666675
 
0.2%
54.333333335
 
0.2%
65.166666675
 
0.2%
Other values (1285)2126
97.0%
(Missing)11
 
0.5%
ValueCountFrequency (%)
6.751
< 0.1%
8.3333333331
< 0.1%
8.9166666671
< 0.1%
91
< 0.1%
9.0416666671
< 0.1%
ValueCountFrequency (%)
1002
0.1%
991
< 0.1%
97.958333331
< 0.1%
96.958333331
< 0.1%
96.083333331
< 0.1%

PREASSURE
Real number (ℝ≥0)

Distinct851
Distinct (%)39.0%
Missing11
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1016.481307
Minimum994.0416667
Maximum1043.458333
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:45.346780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum994.0416667
5-th percentile1000.875
Q11008
median1016.291667
Q31024.677083
95-th percentile1032.752083
Maximum1043.458333
Range49.41666667
Interquartile range (IQR)16.67708333

Descriptive statistics

Standard deviation10.10184942
Coefficient of variation (CV)0.009938057242
Kurtosis-0.9059175939
Mean1016.481307
Median Absolute Deviation (MAD)8.333333333
Skewness0.0914959284
Sum2215929.25
Variance102.0473617
MonotocityNot monotonic
2021-05-04T10:10:45.557226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1020.8758
 
0.4%
1007.8333338
 
0.4%
10148
 
0.4%
1019.1257
 
0.3%
1027.2083337
 
0.3%
1007.8757
 
0.3%
1002.0416677
 
0.3%
1015.9166677
 
0.3%
1020.6666677
 
0.3%
1007.57
 
0.3%
Other values (841)2107
96.2%
(Missing)11
 
0.5%
ValueCountFrequency (%)
994.04166671
< 0.1%
994.45833332
0.1%
994.83333331
< 0.1%
994.95833331
< 0.1%
995.04166671
< 0.1%
ValueCountFrequency (%)
1043.4583331
< 0.1%
1041.7083331
< 0.1%
1039.7083331
< 0.1%
1039.5833331
< 0.1%
1039.51
< 0.1%

TEMPERATURE
Real number (ℝ)

HIGH CORRELATION

Distinct1526
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.89117975
Minimum-4.277322404
Maximum34.5204918
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:45.790310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-4.277322404
5-th percentile3.321721311
Q18.888661202
median19.08333333
Q326.63114754
95-th percentile30.45628415
Maximum34.5204918
Range38.79781421
Interquartile range (IQR)17.74248634

Descriptive statistics

Standard deviation9.385066363
Coefficient of variation (CV)0.5245638629
Kurtosis-1.306470141
Mean17.89117975
Median Absolute Deviation (MAD)8.538251366
Skewness-0.2101688121
Sum39199.57484
Variance88.07947064
MonotocityNot monotonic
2021-05-04T10:10:45.991807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.609289627
 
0.3%
27.314207657
 
0.3%
28.475409846
 
0.3%
21.849726785
 
0.2%
27.997267765
 
0.2%
3.5437158475
 
0.2%
29.090163935
 
0.2%
28.85109295
 
0.2%
9.8278688524
 
0.2%
21.064207654
 
0.2%
Other values (1516)2138
97.6%
ValueCountFrequency (%)
-4.2773224041
< 0.1%
-2.7062841531
< 0.1%
-2.6721311481
< 0.1%
-2.6038251371
< 0.1%
-2.0232240441
< 0.1%
ValueCountFrequency (%)
34.52049181
< 0.1%
34.110655741
< 0.1%
34.110655741
< 0.1%
33.564207652
0.1%
32.983606561
< 0.1%

WIND_SPEED
Real number (ℝ≥0)

Distinct2156
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.26215979
Minimum1.244583333
Maximum463.1879167
Zeros0
Zeros (%)0.0%
Memory size17.2 KiB
2021-05-04T10:10:46.223185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.244583333
5-th percentile2.501875
Q15.679583333
median10.70875
Q321.58041667
95-th percentile84.67416667
Maximum463.1879167
Range461.9433333
Interquartile range (IQR)15.90083333

Descriptive statistics

Standard deviation41.02850686
Coefficient of variation (CV)1.76374452
Kurtosis29.34404446
Mean23.26215979
Median Absolute Deviation (MAD)6.113333333
Skewness4.769878438
Sum50967.3921
Variance1683.338375
MonotocityNot monotonic
2021-05-04T10:10:46.421622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.97253
 
0.1%
6.052
 
0.1%
11.434166672
 
0.1%
5.3841666672
 
0.1%
11.196666672
 
0.1%
8.0841666672
 
0.1%
1.9341666672
 
0.1%
6.053752
 
0.1%
5.606252
 
0.1%
1.6552
 
0.1%
Other values (2146)2170
99.0%
ValueCountFrequency (%)
1.2445833331
< 0.1%
1.41251
< 0.1%
1.4845833331
< 0.1%
1.4866666671
< 0.1%
1.503751
< 0.1%
ValueCountFrequency (%)
463.18791671
< 0.1%
407.35333331
< 0.1%
384.42541671
< 0.1%
365.438751
< 0.1%
365.41166671
< 0.1%

COMMULATIVE_PRECIPITATION
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct179
Distinct (%)8.2%
Missing8
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean459.8928081
Minimum0
Maximum999990
Zeros1742
Zeros (%)79.5%
Memory size17.2 KiB
2021-05-04T10:10:46.629067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.9
Maximum999990
Range999990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21402.67423
Coefficient of variation (CV)46.53839732
Kurtosis2182.99921
Mean459.8928081
Median Absolute Deviation (MAD)0
Skewness46.72257286
Sum1003946
Variance458074464
MonotocityNot monotonic
2021-05-04T10:10:46.879237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01742
79.5%
0.148
 
2.2%
0.231
 
1.4%
0.415
 
0.7%
0.610
 
0.5%
0.89
 
0.4%
0.39
 
0.4%
0.98
 
0.4%
0.78
 
0.4%
0.56
 
0.3%
Other values (169)297
 
13.6%
(Missing)8
 
0.4%
ValueCountFrequency (%)
01742
79.5%
0.148
 
2.2%
0.231
 
1.4%
0.39
 
0.4%
0.415
 
0.7%
ValueCountFrequency (%)
9999901
< 0.1%
2231
< 0.1%
203.61
< 0.1%
102.31
< 0.1%
75.81
< 0.1%

Interactions

2021-05-04T10:10:18.725983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:18.950898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:19.148476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:19.339353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:19.529390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:19.740794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:21.436027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:21.650423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:21.972095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:22.272272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:22.601690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:22.912853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:23.165147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:23.387094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:23.632151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:23.862456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:24.088821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:24.332400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:24.551811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:24.744297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:24.960233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:25.171679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:25.366365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:25.552100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:25.740242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:25.938146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:26.151398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:26.352376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:26.545883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:26.775241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:26.967727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:27.179246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:27.370739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:27.559237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:27.776305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:27.960843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:28.174239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:28.361738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:28.687922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:28.915298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:29.110268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:29.301567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:29.491539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:29.723915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:29.907451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:30.110881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:30.310861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:30.512840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:30.702338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:30.893451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:31.070131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:31.327743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:31.529714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:31.713221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:31.908699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:32.088219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:32.298171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:32.488666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:32.691126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:32.898084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:33.110516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:33.357854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:33.548345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:33.743822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:33.985688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:34.210456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:34.420404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:34.630842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:34.863260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:35.107569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:35.317011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:35.524461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:35.747410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:35.978783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:36.219143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:36.435561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:36.616078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:36.805573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:37.114764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:37.295292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:37.496456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:37.708380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:37.996612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:38.322746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:38.631938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:38.919168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:39.158040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:39.352522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:39.546034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T10:10:39.801321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-04T10:10:47.072216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-04T10:10:47.446949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-04T10:10:47.850838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-04T10:10:48.260768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-04T10:10:40.151919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-04T10:10:40.591232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-04T10:10:40.927376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-04T10:10:41.226575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

FECHASEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATION
02010-01-014NaNNaNNaN129.000000-18.75000038.4583331017.0833332.04098414.4583330.0
12010-01-024NaNNaNNaN144.333333-8.50000077.9375001024.7500003.37295124.8600000.0
22010-01-034NaNNaNNaN78.375000-10.12500087.9166671022.7916670.57240470.93791711.2
32010-01-044NaNNaNNaN29.291667-20.87500046.2083331029.291667-1.852459111.1608330.0
42010-01-054NaNNaNNaN43.541667-24.58333342.0416671033.625000-4.27732256.9200000.0
52010-01-064NaNNaNNaN59.375000-23.70833339.2083331033.750000-2.70628418.5116670.0
62010-01-074NaNNaNNaN72.458333-21.25000049.0000001034.083333-2.67213110.1700000.0
72010-01-084NaNNaNNaN174.333333-17.12500064.5416671028.000000-2.0232241.9729170.0
82010-01-094NaNNaNNaN84.750000-16.33333357.2500001029.0416670.09426213.2987500.0
92010-01-104NaNNaNNaN55.083333-15.95833356.5000001032.5000000.40163917.4158330.0

Last rows

FECHASEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATION
21812015-12-224331.666667351.235294327.666667336.958333-4.41666789.1666671027.9583335.2172133.4791670.0
21822015-12-234275.041667257.217391250.125000254.541667-5.95833370.4583331026.5000007.2663936.2033330.0
21832015-12-244125.347826119.916667108.727273100.416667-6.75000064.2083331027.0000007.3346994.5041670.0
21842015-12-254564.708333518.047619510.043478537.250000-4.00000096.0833331019.2500004.7049182.2670830.0
21852015-12-264266.521739255.083333238.208333254.333333-5.04166786.5833331024.9166675.1147544.3012500.0
21862015-12-27452.79166766.04166757.70833356.208333-13.95833353.5416671038.6250002.9289623.9508330.0
21872015-12-284117.416667119.583333111.833333112.416667-11.45833360.7500001035.0416674.05601113.6566670.0
21882015-12-294323.416667361.500000330.750000331.875000-6.62500076.1250001028.8750005.2855191.2445830.0
21892015-12-30451.791667135.50000094.291667101.750000-8.75000058.4583331030.3750007.30054626.5025000.0
21902015-12-31463.82608783.16666761.30434870.875000-10.08333359.4166671032.4583335.2513669.0733330.0